Analyses of Mitochondrial DNA and Immune Phenotyping Suggest Accelerated T-Cell Turnover in Treated HIV
Bibliographic record
Abstract
BACKGROUND: HIV infection is associated with premature aging, and mitochondrial integrity is compromised during the aging process. Because mitochondrial toxicity is a consequence of antiretroviral therapies (ARTs), we hypothesized HIV and long-term ART would correlate with immunosenescence and mitochondrial DNA (mtDNA) pathology. SETTING: Thirteen older HIV-infected individuals (aged >40 years) with virologic suppression (stratified by duration of ART) were compared with 10 uninfected controls well-matched for age. METHODS: Peripheral blood T-cells were immunophenotyped to measure immune activation, proliferation, and immunosenescence in subsets. mtDNA copies per cell and the relative abundance of mtDNA carrying the "common deletion" (RACD) were quantified by droplet digital polymerase chain reaction. RESULTS: Immune activation was higher in HIV-infected individuals than HIV-uninfected individuals in mature CD4 T-cell subsets (CD4TTM P = 0.025, CD4TEM P = 0.0020) regardless of ART duration. Cell populations from uninfected individuals were more likely to be more senescent populations in mature CD4 T-cell subsets (TTM P = 0.017), and CD8 (CD8TEMRA+ P = 0.0026). No differences were observed in mtDNA or RACD levels in any CD4 T-cell subsets, while CD8TSCM of infected individuals trended to have more mtDNA (P = 0.057) and reduced RACD (P = 0.0025). CONCLUSIONS: HIV-infected individuals demonstrated increased immune activation, but reduced senescence in more mature T-cell subsets. Increased mtDNA content and lower RACD in CD8TSCM suggest immune activation driven turnover of these cells in HIV-infected persons.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".